Phased array radar reflectivity factor simulation method based on sparse sounding data

Abstract

The invention relates to a phased array radar reflectivity factor simulation method based on sparse sounding data, belongs to the field of radio, designs a sparse reconstruction simulation scheme based on acquired multi-point multi-height-layer sparse sounding data, and realizes high-quality restoration and three-dimensional continuous field construction of original sparse observation data. A multivariate dictionary base matrix is constructed; through a sparse representation theory and a Bayesian optimization model, a sparse coefficient reflecting rainfall essential characteristics is obtained, and high-spatial-resolution reconstruction of a temperature and humidity field and a rainfall micro-physical parameter field is realized. Thereafter, a meteorological parameter-driven phase judgment and probability field calculation model is constructed, and three-dimensional simulation of liquid, solid and mixed rainfall echoes is realized. And finally, calculating a unit direction vector and an azimuth guidance coding matrix of a beam ray of the phased array radar, introducing a radar volume scanning data implicit sampling simulator, and realizing high-precision reflectivity factor simulation of the phased array radar in a three-dimensional scanning mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radio, in particular to a phased array radar reflectivity factor simulation method based on sparse sounding data. BACKGROUND

[0002] In the modern meteorological monitoring and forecasting system, the phased array weather radar is gradually becoming an important technical equipment for strong weather identification, quantitative precipitation estimation and radar networking due to its advantages of fast scanning, flexible beam scheduling and strong three-dimensional volume detection capability. As the most core observation product, the radar reflectivity factor directly affects the identification and quantification of precipitation intensity and meteorological disasters. However, in practical application, how to accurately simulate or invert the reflectivity factor data of the phased array radar in different radial and distance banks of multiple antenna elevations based on limited environmental field information is an important research topic in the field of radar remote sensing and numerical simulation, which has significant scientific value and engineering significance.

[0003] At present, the methods for simulating the reflectivity factor of the phased array weather radar mainly include two types: one type is a parameterized simulation method based on an empirical regression relationship, such as constructing an empirical model of Z-R, Z-Nw-Dm to estimate the reflectivity based on the statistical relationship between the temperature and humidity field and the raindrop spectrum parameters; and the other type is a microphysical simulation method based on a physical process, such as calculating the radar scattering echo intensity by using the Mie scattering theory or the T-matrix theory under the premise of known meteorological profile and raindrop spectrum distribution. These methods promote the simulation research of radar data to a certain extent, but also have obvious deficiencies: first, most of the methods rely on ground single-point observation or sounding balloon profile data, which has low spatial coverage and is difficult to reflect the continuous three-dimensional meteorological field changes near the ground; second, the radar simulation process often lacks the ability to depict spatial non-uniformity, resulting in rough echo simulation results and difficulty in supporting high-precision radar data simulation requirements; third, the description of different particle phases in the existing methods is idealized, lacks a probability modeling mechanism, and is difficult to simulate complex mixed phase precipitation systems; fourth, the traditional methods rely on manually set echo functions, which are difficult to embed prior disturbance information under sparse detection data, and the simulation resolution and accuracy are limited. SUMMARY

[0004] The present application aims to overcome the shortcomings of the prior art and provides a phased array radar reflectivity factor simulation method based on sparse sounding data, which solves the problems existing in the prior art.

[0005] The purpose of the present application is achieved by the following technical scheme: a phased array radar reflectivity factor simulation method based on sparse sounding data, the simulation method comprising:

[0006] S1, collect sparse sounding profile data set and auxiliary data, and phased array weather radar spatial position and running state data; generate height uniform vector, and generate spatial base matrix according to the collected data;

[0007] S2, input the data collected in S1, obtain normalized atmospheric temperature profile by adopting inverse distance weighted interpolation method and normalization processing, and obtain attribute disturbance frequency spectrum base matrix by combining 1D discrete Fourier transform model; generate multi-scale disturbance base matrix according to the normalized atmospheric temperature profile and wavelet transform model, and generate complete dictionary base matrix of atmospheric temperature according to the spatial base matrix, the attribute disturbance frequency spectrum base matrix and the multi-scale disturbance base matrix; update the atmospheric temperature profile into profile data of atmospheric humidity, mass weighted average diameter of precipitation particles and normalized intercept of precipitation particle spectrum, and obtain complete dictionary base matrix of atmospheric humidity, mass weighted average diameter of precipitation particles and normalized intercept of precipitation particle spectrum;

[0008] S3, obtain sparse coefficient vector of each observation point by using sparse representation model and combining calculation model of likelihood function and sparse prior according to the data obtained in S2, and optimize by using the sparse representation model, so as to realize optimization of data of atmospheric temperature, atmospheric humidity, mass weighted average diameter of precipitation particles and normalized intercept of precipitation particle spectrum;

[0009] S4, process the data optimized in S3, and obtain calculation result by combining structure similarity weight coefficient calculation model and spatial weight coefficient calculation model; multiply the calculation result, normalize, and obtain high spatial resolution three-dimensional data set of atmospheric temperature, atmospheric humidity, mass weighted average diameter of precipitation particles and normalized intercept of precipitation particle spectrum according to weighted projection formula;

[0010] S5, filter the data obtained in S4, process the filtered data, and calculate high spatial resolution phased array weather radar reflectivity factor three-dimensional data by combining the data of S4 and precipitation particle phase state probability calculation formula and multi-phase state radar reflectivity factor joint calculation formula;

[0011] S6, generate spatial position matrix according to the data collected in S1, combine the calculation of each point in the spatial position matrix with the azimuthal heading encoding vector of each point by column to form an azimuthal heading encoding matrix, and obtain radar reflectivity factor data in the phased array weather radar stereoscopic scanning mode by combining the data collected in S1 and the high spatial resolution phased array weather radar reflectivity factor three-dimensional data obtained in S5.

[0012] The collection of sparse sounding profile data set and auxiliary data, and phased array weather radar spatial position and running state data specifically includes the following contents:

[0013] The sparse sounding profile data set and auxiliary data obtained by the meteorological unmanned aerial vehicle array are collected, and the sparse sounding profile data set obtained by the meteorological unmanned aerial vehicle array includes: the total number of ground fixed points corresponding to the meteorological unmanned aerial vehicle array is M, the atmospheric temperature profile data set T_TR i , the atmospheric humidity profile data set Q_TR i , the mass-weighted mean diameter profile data set Dm_TR i of the precipitation particles, the normalized intercept profile data set Nw_TR i of the precipitation particle spectrum, i = 1, 2, 3, … M; the auxiliary data of the meteorological unmanned aerial vehicle array includes: the spatial position information of the observation point at different height layers at the i-th meteorological unmanned aerial vehicle ground fixed point is P i = (Lon_TR i , Lat_TR i , Hig_TR i ), wherein Lon_TR i is a longitude point, Lat_TR i is a latitude point, and Hig_TR i represents a vector composed of different heights, i = 1, 2, 3, …, M;

[0014] The spatial position and operating state data of the phased array weather radar are collected, mainly including: the spatial position information P_radar=[lon_radar,lat_radar,hig_radar] of the phased array weather radar antenna feed, wherein lon_radar, lat_radar and hig_radar are longitude, latitude and height respectively; radar wavelength wavelength, radar radial distance resolution delt_R, beam width delt_sita, radar scanning sequence el_array=[el0,el1,el2,…,el max ] on the elevation layer, el max is the maximum scanning elevation angle, and Rmax_radar is the maximum unambiguous distance.

[0015] The generation of the height-unified vector and the generation of the spatial basis matrix according to the collected data specifically include the following contents:

[0016] A1, setting the resolution and multiple of the height layer as delt_H and Num_H respectively, generating the maximum height layer as Hig_max=Num_H×delt_H, and then generating the height-unified vector Z i =h×delt_H, wherein h = 1, 2, 3, …, Num_H, i = 1, 2, 3, …, M;

[0017] A2, according to the collected Pi = (Lon_TR i , Lat_TR i , Hig_TR i ), replace Hig_TR i in P i with Z i , generate each meteorological unmanned aerial vehicle ground fixed point after the height of the profile data space location information P i_uni = (Lon_TR i , Lat_TR i , Z i );

[0018] A3, set the observation point P j as any one of the space points in P i_uni , normalize the longitude, latitude and height of the observation point P j in longitude, latitude and height dimensions respectively, get the normalized longitude , latitude and height , and the normalized =( ), j = 1, 2, 3,.., MxNum_H;

[0019] A4, according to the second order polynomial orthogonal basis function, generate the spatial structure vector of the observation point P j , represents the first second order polynomial orthogonal basis function;

[0020] A5, according to the method of A3 and A4 steps, MxNum_H P j is processed respectively, and the processed MxNum_H space structure vectors are combined to generate the space base matrix Φs=[ ], the dimension is MxNum_Hx10.

[0021] The data collected by S1 is taken as input, the inverse distance weighted interpolation method and normalization processing are used to obtain the normalized atmospheric temperature profile, and the 1D discrete Fourier transform model is combined to obtain the attribute disturbance frequency spectrum base matrix, which specifically includes the following contents:

[0022] B1, take the collected atmospheric temperature profile data set T_TR i of different height layers on the i-th meteorological unmanned aerial vehicle ground fixed point and the space position information P i corresponding to the profile as input, use the inverse distance weighted interpolation method to interpolate T_TR i to the generated space information P i_uniThe spatial position of the target profile is obtained after interpolation of the atmospheric temperature profile data at each meteorological unmanned aerial vehicle ground fixed point, denoted as ;

[0023] B2, the average value and the standard deviation of the profile data are calculated, denoted as mean( ) and std( ), and then the profile data is normalized according to the Z-Score normalization formula, to obtain the normalized atmospheric temperature profile ;

[0024] B3, the atmospheric temperature profile is input into the 1D discrete Fourier transform model, the model is run, and the output result of the model is taken as a modulus to obtain the frequency spectrum modulus sequence DFT of the atmospheric temperature profile i =|DFT( )| ;

[0025] B4, the modulus values of the high frequency part in DFT i are set to zero, the modulus values of the low frequency part are retained, the first Ka low frequency components of DFT i except the direct current component are selected and denoted as DFT i,2 , DFT i,3 , …, DFT i,Ka+1 , and the Ka low frequency components are combined into a one-dimensional vector, that is, a disturbance modal vector c i is formed, and Ka is the number of selected low frequency components;

[0026] B5, the method of B1-B4 is applied to the atmospheric temperature profile data at different height layers of M meteorological unmanned aerial vehicle ground fixed points, to obtain M disturbance modal vectors, denoted as c1, c2, c3, …, c M , and the M disturbance modal vectors are spliced into an attribute disturbance frequency spectrum base matrix Φc=[c1;c2;c3;…;c M ], and the dimension is M×Num_H×Ka.

[0027] The method according to the application further comprises the following steps:

[0028] C1, the generated normalized atmospheric temperature profile is input into the wavelet transform model, the type of wavelet basis is selected, the decomposition layer number is set as La, the model is run, and the model output is the and , wherein is the approximation coefficient of the La layer, is the detail coefficient of the r layer, r = 1, 2, 3, …, La;

[0029] C2, the calculation formula of the disturbance energy is extracted according to the detail coefficient, and the disturbance energy b i,r of the La decomposition layers at the i th point is extracted, r = 1, 2, 3, …, La; then, the disturbance energy b i,1 of all the La decomposition layers at the i th point is combined into a multi-scale disturbance feature vector m i,2 ; i,3 i,La i ;

[0030] C3, the method of C1 and C2 is applied to the normalized atmospheric temperature profiles of different height layers at M meteorological unmanned vehicle ground fixed points, M multi-scale feature vectors are obtained, and the M multi-scale feature vectors are spliced into a multi-scale disturbance basis matrix Φ m = [m 1 ; m 2 ; m 3 ; …; m M ], m M is the M th multi-scale feature vector, and the dimension is M × Num_H × La;

[0031] C4, the generated Φ s, Φ c and Φ m generated in this step are used to construct a complete dictionary basis matrix Φ T = [Φ s ||Φ c ||Φ m ] of the atmospheric temperature, the atmospheric temperature profile data set is updated into an atmospheric humidity profile data set, a mass-weighted average diameter profile data set of precipitation particles, and a normalized intercept profile data set of precipitation particle spectrum, and then combined with Φ s generated by A5 of S1, a complete dictionary basis matrix Φ Q of the atmospheric humidity normalized intercept, a complete dictionary basis matrix Φ Dm of the mass-weighted average diameter of the precipitation particles, and a complete dictionary basis matrix Φ Nw of the normalized intercept of the precipitation particle spectrum are generated, respectively.

[0032] S3 specifically includes the following contents:

[0033] S301, the steps of B1 are applied to the atmospheric temperature profile data of M meteorological unmanned vehicle ground fixed points, and M interpolated atmospheric temperature profile data are obtained, denoted as ;

[0034] ​​S302, according to the generated atmospheric temperature complete dictionary basis matrix Φ T , using the sparse representation model, the observation data d of each observation point g is expressed in the form of a linear combination of Φ g , T and sparse coefficient vector a g , g Input d g into the calculation model combined with the likelihood function and the sparse prior, run the model, and get the sparse coefficient vector a g of each observation point T , Q Input a Dm into the Bayesian inference and maximum a posteriori estimation model with sparse constraint conditions, run the model, and get the optimized sparse coefficient vector ;

[0035] S303, based on the generated dictionary basis Φ and the optimized , using the optimized sparse representation model, optimize the atmospheric temperature data set, and the optimized atmospheric temperature data set is denoted as ;

[0036] S304, update Φ Nw and the atmospheric humidity profile data set, Φ h and the mass-weighted mean diameter profile data set of the precipitation particles, and Φ h and the normalized intercept profile data set of the precipitation particle spectrum, respectively, to realize the optimization of the atmospheric humidity, the mass-weighted mean diameter of the precipitation particles, and the normalized intercept data of the precipitation particle spectrum, respectively, denoted as , , .

[0037] The S4 specifically includes the following contents:

[0038] S401, according to the generated optimized atmospheric temperature data set , group the data set according to the height layer to form a two-dimensional sparse plane data set T_Z k of multiple height layers, h=1, 2, 3, …, Num_H;

[0039] S402, according to the set height layer resolution delt_H, and set the horizontal space high spatial resolution as delt_R_H, generate a target three-dimensional grid Tar_grid with a spatial resolution of delt_R_H×delt_R_H×delt_H, for T_Z k and the kth height layer data set of Tar_grid, denoted as T_Z k and Tar_grid_k respectively, calculate T_Zk The maximum, median, and minimum values ​​are denoted as max(T_Z). k ), med(T_Z k ), min(T_Z k );

[0040] S403, T_Z k With max(T_Z) k ), med(T_Z k ), min(T_Z k The inputs are combined with the inputs into the structural similarity weight coefficient calculation model. Running the model outputs T_Z. k The set of structural weight coefficients W1_T_Z for each sparse point k Calculate the relationship between a point pt and T_Z in Tar_grid_k. k The spatial distances between all sparse observation points are calculated, and the results are input into the spatial weight coefficient calculation model. The model is then run, and the output is T_Z. k The set of spatial weight coefficients for each sparse point in W2_T_Z k W1_T_Z k With W2_T_Z k Multiply and normalize to form the total weight coefficient set W_T_Z k According to the weighted projection formula, the atmospheric temperature value T_grid_pt at point pt in Tar_grid_k is calculated;

[0041] S404. Apply the method of step S403 to all points in Tar_grid_k, and apply this step to all height layers in Tar_grid to obtain a high spatial resolution atmospheric temperature 3D dataset T_3D. Update the atmospheric temperature dataset of this step sequentially to atmospheric humidity, mass-weighted average diameter of precipitation particles, and normalized intercept dataset of precipitation particle spectrum, to obtain high spatial resolution atmospheric humidity, mass-weighted average diameter of precipitation particles, and normalized intercept dataset of precipitation particle spectrum, denoted as Q_3D, Dm_3D, and Nw_3D, respectively.

[0042] S5 specifically includes the following:

[0043] S501. Based on the generated T_3D, Q_3D, Dm_3D and Nw_3D datasets, set the variation ranges of Dm_3D and Nw_3D to [Dm_min, Dm_max] and [Nw_min, Nw_max] respectively, and remove the data in the Dm_3D and Nw_3D datasets that exceed the variation range;

[0044] S502, input the Dm_3D and Nw_3D data sets after removing outliers into a 3-point x 3-point x 3-point median filter respectively, and the filtered outputs are denoted as Dm_3D_f and Nw_3D_f respectively, and input the T_3D and Q_3D data sets into a spatial Gaussian smoothing filter respectively, and the outputs are the filtered atmospheric temperature and atmospheric humidity data sets, denoted as T_3D_f and Q_3D_f respectively;

[0045] S503, set the atmospheric humidity threshold for precipitation occurrence as Q_th, mark all grid points with atmospheric humidity values greater than Q_th in Q_3D_f as 1, and mark the remaining grid points as 0 to generate a precipitation identification mask matrix Rain_Mask, and set the phase state of the precipitation particles as liquid, solid-liquid mixed, and solid;

[0046] S504, for any point P_s in the established target three-dimensional grid Tar_grid, obtain the atmospheric temperature and atmospheric humidity data of the point by combining T_3D_f and Q_3D_f and , and then calculate the probabilities of the three phase states of liquid, solid-liquid mixed, and solid for the space point P_s respectively using the precipitation particle phase state probability calculation formula, and denote them as P_L s , P_M s , and P_I s respectively.

[0047] S505, apply the steps of S504 to all space points in the target three-dimensional grid Tar_grid to generate probability matrices for the three phase states of liquid, solid-liquid mixed, and solid, denoted as P_L, P_M, and P_I respectively, set the related indices of the liquid, solid-liquid mixed, and solid particles as alph_L, alph_M, and alph_I respectively, and then input [alph_L, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], [alph_M, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], and [alph_I, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask] together with the collected radar wavelength wavelength into a phase state sensitive radar echo simulation system, run the system, and output the radar reflectivity factor three-dimensional data sets Z_L, Z_M, and Z_I for all points in the target three-dimensional grid Tar_grid when the phase states of the precipitation particles are liquid, solid-liquid mixed, and solid respectively, and calculate the high spatial resolution phased array weather radar reflectivity factor three-dimensional data Z_grid according to the calculated P_L, P_M, P_I, Z_L, Z_M, and Z_I, and the multi-phase state radar reflectivity factor joint calculation formula.

[0048] The S6 specifically comprises the following contents:

[0049] S601, according to the collected phased array weather radar radial distance resolution delt_R, beam width delt_sita, maximum unambiguous distance Rmax_radar and the elevation scanning sequence el_array of the radar, a distance library sequence bins_array=[delt_R, 2×delt_R, 3×delt_R, …, Rmax_radar], an azimuth sequence az_array=[delt_sita, 2×delt_sita, 3×delt_sita, …, 360°] are generated, el_array, az_array and bins_array are combined to generate the spatial position matrix P_echo=[el_array, az_array, bins_array] of the echo under the three-dimensional scanning mode of the phased array weather radar;

[0050] S602, set a point in P_echo as P0, and the spatial position of the point is [el_0, az_0, bin_0], wherein el_0, az_0 and bin_0 are the elevation angle, azimuth angle and distance library of the point respectively, then according to the unit direction vector calculation formula, the unit direction vector dr of the radar beam ray is calculated, and according to the polar coordinate angle coding formula, the polar coordinate angle coding E0_dir is calculated, and according to the three-dimensional relative coordinate coding calculation formula, the three-dimensional relative coordinate coding E0_rel is calculated, and the calculated E0_rel, E0_dir and dr are combined to generate the azimuth guide coding vector E0 of the point P0.

[0051] S603, the steps of S602 are applied to each point in P_echo, and the azimuth guide coding vector of each point generated is combined in columns to form the azimuth guide coding matrix E_ALL, and the spatial position information P_radar of the collected radar antenna feed, the generated Z_grid and the azimuth guide coding matrix E_ALL are input into the volume scanning data implicit sampling simulator together, the simulator is run, and the output is the radar reflectivity factor data under the three-dimensional scanning mode of the phased array weather radar.

[0052] The present application has the following advantages:

[0053] 1. In terms of data sources, the application uses meteorological unmanned aerial vehicle array to obtain multivariate vertical profile observation data above multiple ground fixed points, including atmospheric temperature, atmospheric humidity, mass weighted average diameter (Dm) of precipitation particles and normalized intercept (Nw), realizes spatial expansion and vertical fine description of sounding data, and significantly improves the physical basis and spatial coverage ability of reflectivity simulation. Secondly, in the modeling method, the application combines three types of dictionary bases of spatial position base function, attribute disturbance frequency spectrum base and multi-scale wavelet energy base, constructs a high-dimensional expression system with strong physical interpretability, realizes high-precision reconstruction of original sparse data through sparse representation and Bayesian optimization, and effectively solves the problems of data sparsity and discontinuity in traditional methods.

[0054] 2. The particle phase probability modeling mechanism is introduced, different microphysical response models of liquid state, mixed state and solid state are combined, a phase-sensitive radar echo simulation module is introduced, and a more realistic and continuous reflectivity three-dimensional field is generated through a multi-phase state weighted fusion formula, which overcomes the shortcomings of rough or static setting of phase state in traditional methods. Finally, the method constructs an azimuth guide coding matrix, fully considers the spatial sampling characteristics of phased array radar under different elevation angles, azimuth angles and distance resolutions, so that the simulation output can accurately match the actual radar observation mode, and the spatial consistency and system adaptation ability of reflectivity simulation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The flowchart of the application is shown in the figure;

[0056] Figure 2 The implementation flowchart of the spatial base matrix generation of the application is shown in the figure;

[0057] Figure 3 The implementation flowchart of the attribute disturbance frequency spectrum base matrix generation of the application is shown in the figure;

[0058] Figure 4 The implementation flowchart of the complete dictionary base matrix generation of the application is shown in the figure;

[0059] Figure 5 The implementation flowchart of the meteorological unmanned aerial vehicle array observation data optimization of the application is shown in the figure;

[0060] Figure 6 The implementation flowchart of the high spatial resolution three-dimensional data set generation based on meteorological unmanned aerial vehicle observation data of the application is shown in the figure;

[0061] Figure 7 The implementation flowchart of the high spatial resolution phased array weather radar reflectivity factor three-dimensional data generation of the application is shown in the figure;

[0062] Figure 8This is a flowchart illustrating the implementation of radar reflectivity factor data generation in the phased array weather radar stereo scanning mode of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Therefore, the detailed description of the embodiments of this application provided below with reference to the accompanying drawings is not intended to limit the scope of protection of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The present invention will be further described below with reference to the accompanying drawings.

[0064] This invention specifically relates to a method for simulating the reflectivity factor of phased array weather radar based on sparse radiosonde data from meteorological UAV arrays. It utilizes multiple meteorological UAVs to construct a sparse upper-air profile data grid, acquiring atmospheric temperature profiles, atmospheric humidity profiles, mass-weighted average diameters of precipitation particles, and normalized intercepts, among other environmental field and microphysical variables, over different fixed ground points. A spatial-attribute-multi-scale joint perturbation dictionary basis matrix is ​​then constructed based on the spatial location. This method not only achieves high-quality reconstruction of the original sparse data through sparse representation and Bayesian inference, but also realizes high-precision three-dimensional simulation of radar reflectivity for complex precipitation fields by constructing a particle phase probability field and integrating sensitive reflectivity models for liquid, solid, and mixed states.

[0065] like Figure 1 As shown, it specifically includes the following:

[0066] Step 1: Collect sparse sounding profile datasets and auxiliary data obtained by meteorological drone arrays; collect spatial location and operational status data of phased array weather radars;

[0067] The sparse sounding profile dataset acquired by the meteorological UAV array mainly includes: the total number of ground fixed points corresponding to the meteorological UAV array is M=100; and the atmospheric temperature profile dataset T_TR at different altitudes at the i-th meteorological UAV ground fixed point. i Atmospheric humidity profile dataset Q_TR i Mass-weighted average diameter profile dataset of precipitation particles Dm_TR i Normalized intercept profile dataset of precipitation particle spectra Nw_TR i , i=1,2,3,…M;

[0068] The auxiliary data of the meteorological UAV array mainly includes: the spatial position information of the observation points of different height layers on the ground fixed point of the i-th meteorological UAV is P i = (Lon_TR i , Lat_TR i , Hig_TR i ), wherein Lon_TR i is a longitude point, Lat_TR i is a latitude point, and Hig_TR i represents a vector composed of different heights, i = 1, 2, 3, …, M;

[0069] The spatial position and operating state data of the phased array weather radar mainly include: the spatial position information of the antenna feed of the phased array weather radar P_radar = [lon_radar, lat_radar, hig_radar], wherein lon_radar, lat_radar and hig_radar are longitude, latitude and height respectively; the radar wavelength wavelength = 3 cm, the radar radial distance resolution deltat_R = 30 meters, and the beam width deltat_sita = 1 degree; the scanning sequence of the radar on the elevation layer el_array = [0.5:0.5:18], the maximum antenna scanning elevation angle el_max = 18 degrees, and the maximum unambiguous distance Rmax_radar = 60 km;

[0070] Step 2: as shown in Figure 2 , first, the resolution and the multiple of the height layer are set as deltat_H = 30 meters and Num_H = 200 respectively, the maximum height layer Hig_max = Num_H x deltat_H is generated, and then the height uniform vector Z i = h x deltat_H (h = 1, 2, 3, …, Num_H, i = 1, 2, 3, …, M) on each UAV ground fixed point is generated; second, according to the P i = (Lon_TR i , Lat_TR i , Hig_TR i ) collected in step 1, Hig_TR i in P i is replaced by Z i , and the profile data spatial position information P i_uni = (Lon_TR i , Lat_TR i , Z i ) after height unification on each meteorological UAV ground fixed point is generated. Then, it is assumed that the observation point P j (j = 1, 2, 3, …, M x Num_H) is P i_uniany one spatial point in the space, the longitude, latitude and height of the observation point P j are normalized in longitude, latitude and height dimensions respectively to obtain normalized longitude , latitude and height , and normalized ( ); then, the spatial structure vector of the observation point P j is generated according to the second-order polynomial orthogonal basis function; finally, the MxNum_H P j is processed respectively according to the above method, and the processed MxNum_H spatial structure vectors are combined to generate the spatial basis matrix Φs[ ] with a dimension of MxNum_Hx10;

[0071] Further, the normalization in each dimension is: (f j -f min ) / (f max -f min ), wherein f j and are the dimension variables to be normalized and normalized respectively, and f max and f min are the maximum and minimum values of f j ;

[0072] The second-order polynomial orthogonal basis function is: ;

[0073] Step 3: as shown in Figure 3 , the atmospheric temperature profile data set T_TR i of different height layers on the i-th meteorological unmanned aerial vehicle ground fixed point collected in step 1 and the spatial position information P i corresponding to the profile are input, the T_TR i is interpolated to the spatial position of the target profile P i_uni generated in step 2 using the inverse distance weighted interpolation method, to obtain the interpolated atmospheric temperature profile data on each meteorological unmanned aerial vehicle ground fixed point, denoted as ; then, the mean and standard deviation of the profile data are calculated, denoted as mean( ) and std( ), and then the profile data is normalized according to the Z-Score normalization formula to obtain the normalized atmospheric temperature profile Then, the atmospheric temperature profile was... The data is input into a 1D discrete Fourier transform model, the model is run, and the modulus of the model's output is taken to obtain the atmospheric temperature profile. Spectral mode sequence DFT i =|DFT( After that, the DFT will be... i The modulus of the mid-to-high frequency range is set to zero, while the modulus of the low frequency range is retained, and the DFT is selected. i The first Ka low-frequency components, excluding the DC component, are denoted as DFT. i,2 DFT i,3 ..., DFT i,Ka+1 These Ka low-frequency components are then combined into a one-dimensional vector, forming the perturbation mode vector c. i Finally, the above method is applied to atmospheric temperature profile data at different altitudes from M fixed ground points of meteorological UAVs to obtain M disturbance mode vectors. These M disturbance mode vectors are then concatenated into an attribute disturbance spectrum basis matrix Φc=[c1;c2;c3;…;c…]. M The dimension is M×Num_H×Ka, where Ka is the number of low-frequency components selected;

[0074] The Z-Score normalization formula is as follows: ;

[0075] Perturbation mode vector c i =[DFT i,2 DFT i,3 ,…,DFT i,Ka+1 ], i=1,2,3,…,M;

[0076] Step 4: As Figure 4 As shown, firstly, the normalized atmospheric temperature profile generated in step 3 is... The data is input into a wavelet transform model. The wavelet basis type is selected as multi-scale sliding window residual basis, and the number of decomposition levels is set to La=3. The model is run, and the output is the atmospheric temperature profile at point i. and ,in These are the approximation coefficients for the La-th layer. Let be the detail coefficients of the r-th layer, where r = 1, 2, 3, ..., La; then, based on the calculation formula for extracting perturbation energy from the detail coefficients, extract the perturbation energy b of the La-th decomposition layer at the i-th point. i,r r=1,2,3,…,La; then, the perturbation energies of all La decomposition layers at point i are combined into a multi-scale perturbation eigenvector m. i; finally, the above method is applied to the normalized atmospheric temperature profile of different height layers on M meteorological unmanned aerial vehicle ground fixed points to obtain M multi-scale feature vectors, and the M multi-scale feature vectors are spliced into a multi-scale disturbance basis matrix Φm=[m1;m2;m3;…;m M ]m M is a low M multi-scale feature vector, and the dimension is MxNum_HxLa. The Φs, Φc and Φm generated in steps 2 and 3 and the Φm generated in this step are combined to construct a complete dictionary basis matrix Φ of atmospheric temperature T =[Φs||Φc||Φm]. The atmospheric temperature profile data set in steps 3 and 4 is updated into an atmospheric humidity profile data set, a mass-weighted average diameter profile data set of precipitation particles, and a normalized intercept profile data set of precipitation particle spectrum, and then combined with the Φs generated in step 2 to generate a complete dictionary basis matrix Φ of the normalized intercept of atmospheric humidity Q , a complete dictionary basis matrix Φ of the normalized intercept of the mass-weighted average diameter of precipitation particles Dm , and a complete dictionary basis matrix Φ of the normalized intercept of the precipitation particle spectrum Nw ;

[0077] The calculation formula of the detail coefficient extraction disturbance energy is: the disturbance energy of the rth decomposition layer scale at the ith point ;

[0078] The multi-scale disturbance feature vector m i =[b i,1 ,b i,2 ,b i,3 ,…,b i,La ];

[0079] Step 5: as shown in Figure 5 , first, step 3 is applied to the atmospheric temperature profile data of M meteorological unmanned aerial vehicle ground fixed points to obtain M interpolated atmospheric temperature profile data, denoted as ; second, according to the complete dictionary basis matrix Φ of atmospheric temperature T generated in step 4, the observation data d g of each observation point g (g=1, 2, 3, …, MxNum_H) in is represented in the form of linear combination of Φ T and sparse coefficient vector a g ; then, d g is input into the calculation model combined with the likelihood function and the sparse prior, and the model is run to obtain the sparse coefficient vector a g of each observation point; then, a gThe input is fed into a Bayesian inference and maximum a posteriori estimation model with sparse constraints, and the model is run to obtain the optimized sparse coefficient vector. Finally, based on the generated dictionary base Φ and the optimized... An optimized sparse representation model is used to optimize the atmospheric temperature dataset. The optimized atmospheric temperature dataset is denoted as... The complete dictionary base matrix Φ used in this step. T The atmospheric temperature profile dataset was updated to Φ. Q And atmospheric humidity profile dataset, Φ Dm And the mass-weighted average diameter profile dataset of precipitation particles, Φ Nw The dataset of normalized intercept profiles of precipitation particle spectra can be used to optimize atmospheric humidity, mass-weighted average diameter of precipitation particles, and normalized intercept data of precipitation particle spectra, respectively denoted as . , , ;

[0080] The sparse representation model is as follows: ,in Gaussian noise with zero mean.

[0081] The sparsity constraint is: , for Norm regularization term, T denotes transpose;

[0082] The optimized sparse representation model is as follows: ;

[0083] Step 6: As Figure 6 As shown, firstly, based on the optimized atmospheric temperature dataset generated in step 5... The dataset is grouped according to height levels to form a two-dimensional sparse plane dataset T_Z with multiple height levels. h h=1,2,3,…,Num_H; secondly, based on the height layer resolution delt_H set in step 2, the horizontal space height resolution is set to delt_R_H, which generates a target 3D mesh Tar_grid with a spatial resolution of delt_R_H×delt_R_H×delt_H; then, for T_Z h Let T_Z be the k-th height layer dataset of Tar_grid. k And Tar_grid_k, calculate T_Z k The maximum, median, and minimum values ​​are denoted as max(T_Z). k ), med(T_Z k ), min(T_Z kAfter that, T_Z k With max(T_Z) k ), med(T_Z k ), min(T_Z k The inputs are combined with the inputs into the structural similarity weight coefficient calculation model. Running the model outputs T_Z. k The set of structural weight coefficients W1_T_Z for each sparse point k Then, calculate the relationship between point pt and T_Z in Tar_grid_k. k The spatial distances between all sparse observation points are calculated, and the results are input into the spatial weight coefficient calculation model. The model is then run, and the output is T_Z. k The set of spatial weight coefficients for each sparse point in W2_T_Z k After that, W1_T_Z k With W2_T_Z k Multiply and normalize to form the total weight coefficient set W_T_Z k Finally, based on the weighted projection formula, the atmospheric temperature value T_grid_pt at point pt in Tar_grid_k is calculated. Applying the above steps to all points in Tar_grid_k and iterating through all height layers in Tar_grid yields a high spatial resolution 3D atmospheric temperature dataset T_3D. This atmospheric temperature dataset is then sequentially updated with atmospheric humidity, the mass-weighted average diameter of precipitation particles, and the normalized intercept of the precipitation particle spectrum. This results in high spatial resolution 3D datasets for atmospheric humidity, the mass-weighted average diameter of precipitation particles, and the normalized intercept of the precipitation particle spectrum, denoted as Q_3D, Dm_3D, and Nw_3D, respectively.

[0084] The weighted projection formula is: , and T_Z k and W_T_Z k The value of the m-th point;

[0085] Step 7: As Figure 7 As shown, firstly, based on the T_3D, Q_3D, Dm_3D, and Nw_3D datasets generated in step 6, the reasonable variation ranges [Dm_min, Dm_max] and [Nw_min, Nw_max] of Dm_3D and Nw_3D are set to [0.1mm, 3.5mm] and [10mm, 3.5mm], respectively. 2 10 6.5Dm_3D and Nw_3D data sets beyond the reasonable change range are removed. Second, the Dm_3D and Nw_3D data sets after removing the abnormal values are respectively input into a 3-point x 3-point x 3-point median filter, and the filtered outputs are respectively denoted as Dm_3D_f and Nw_3D_f. The T_3D and Q_3D data sets are respectively input into a spatial Gaussian smoothing filter, and the outputs are the filtered atmospheric temperature and atmospheric humidity data sets, which are respectively denoted as T_3D_f and Q_3D_f. Then, the atmospheric humidity threshold for precipitation occurrence is set as Q_th, all grid points with atmospheric humidity values greater than Q_th in Q_3D_f are marked as 1, and the remaining grid points are marked as 0 to generate a precipitation identification mask matrix Rain_Mask. Then, the phase state of the precipitation particles is set as liquid, solid-liquid mixed, and solid. and The precipitation particle phase state probability calculation formula is used to calculate the probabilities of the three phase states of liquid, solid-liquid mixed, and solid for the space point P_s, respectively, which are respectively denoted as P_L s , P_M s , and P_I s . The above method is applied to all space points in the target three-dimensional grid Tar_grid to generate probability matrices of the three phase states of liquid, solid-liquid mixed, and solid, which are respectively denoted as P_L, P_M, and P_I. Then, the related indices of the liquid, solid-liquid mixed, and solid particles are respectively set as alph_L=7.0, alph_M=6.0, and alph_I=5.5, and [alph_L, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], [alph_M, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], [alph_I, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], and the radar wavelength wavelength collected in step 1 are respectively input into the phase state sensitive radar echo simulation system, the system is run, and the outputs are the radar reflectivity factor three-dimensional data sets Z_L, Z_M, and Z_I of all points in the target three-dimensional grid Tar_grid when the phase states of the precipitation particles are respectively liquid, solid-liquid mixed, and solid. Finally, according to the calculated P_L, P_M, P_I, Z_L, Z_M, and Z_I, and according to the multi-phase radar reflectivity factor joint calculation formula, the high spatial resolution phased array weather radar reflectivity factor three-dimensional data Z_grid is calculated.

[0086] wherein the precipitation particle phase state probability calculation formula is: ,

[0087] ,

[0088] ,

[0089] ,

[0090] wherein is the fuzzy degree control factor, when a is 1 or 2, it is to distinguish the input is temperature or humidity, to calculate the probability, F represents the meaning of function, X is the input variable;

[0091] The joint calculation formula of the multi-phase radar reflectivity factor is: Z_grid=Z_L×P_L+Z_M×P_M+Z_I×P_I;

[0092] Step 8: as Figure 8As shown, first, according to the radial distance resolution delt_R, the beam width delt_sita, the maximum unambiguous distance Rmax_radar and the elevation scan sequence el_array of the phased array weather radar collected according to step 1, a distance bin sequence bins_array=[delt_R, 2×delt_R, 3×delt_R, …, Rmax_radar], an azimuth sequence az_array=[delt_sita, 2×delt_sita, 3×delt_sita, …, 360°] are generated, el_array, az_array and bins_array are combined to generate a spatial position matrix P_echo=[el_array, az_array, bins_array] of the echo under the three-dimensional scanning mode of the phased array weather radar. Second, assuming that a certain point in P_echo is P0, the spatial position of the point is [el_0, az_0, bin_0], where el_0, az_0 and bin_0 are the elevation angle, azimuth angle and distance bin of the point respectively, then the unit direction vector dr of the radar beam ray is calculated according to the unit direction vector calculation formula, the polar coordinate angle coding E0_dir is calculated according to the polar coordinate angle coding formula, and the three-dimensional relative coordinate coding E0_rel is calculated according to the three-dimensional relative coordinate coding calculation formula; then, the calculated E0_rel, E0_dir and dr are combined to generate the azimuth guidance coding vector E0 of the point P0. The above processing procedure is applied to each point in P_echo, and the azimuth guidance coding vector of each point generated is combined in columns to form the azimuth guidance coding matrix E_ALL. Then, the spatial position information P_radar of the radar antenna feed collected in step 1, the Z_grid generated in step 7 and the azimuth guidance coding matrix E_ALL generated in this step are input into the volume scanning data implicit sampling simulator together, the simulator is run, and the radar reflectivity factor data under the three-dimensional scanning mode of the phased array weather radar is output.

[0093] The unit direction vector calculation formula is: dr=[cos(el_0)×sin(az_0), cos(el_0)×cos(az_0), sin(el_0)].

[0094] The polar coordinate angle coding formula is: E0_dir=[sin(k1×az_0), cos(k1×az_0), sin(k1×el_0), cos(k1×el_0), …, sin(k1×el_0), cos(k1×el_0)], where k1 and k2 are 2 and 2 respectively. n n n n ​​​Frequency increments of the frequency base.

[0095] The three-dimensional relative coordinate coding calculation formula is: E0_rel=bin_0×dr.

[0096] The above only describes the preferred embodiments of the present application, and it should be understood that the present application is not limited to the forms disclosed herein, should not be considered as excluding other embodiments, and can be used in various other combinations, modifications and improvements, and can be changed within the scope of the concepts described herein, by the above teachings or related art or knowledge. Changes and variations made by those skilled in the art without departing from the spirit and scope of the present application shall be within the scope of the appended claims of the present application.

Claims

1. A method for simulating reflectivity factors of phased array radar based on sparse radiosonde data, characterized in that: The simulation method comprises: S1, collecting sparse sounding profile data sets and auxiliary data, and phased array weather radar spatial position and operating state data; generating height uniform vectors, and generating a spatial base matrix according to the collected data; S2, taking the data collected in S1 as input, obtaining normalized atmospheric temperature profiles by adopting inverse distance weighted interpolation and normalization processing, and obtaining an attribute disturbance frequency spectrum base matrix in combination with a 1D discrete Fourier transform model; generating a multi-scale disturbance base matrix according to the normalized atmospheric temperature profiles and a wavelet transform model, and generating a complete dictionary base matrix of atmospheric temperature according to the spatial base matrix, the attribute disturbance frequency spectrum base matrix and the multi-scale disturbance base matrix; updating the atmospheric temperature profile into normalized intercept profile data of atmospheric humidity, mass-weighted average diameter of precipitation particles and precipitation particle spectrum to obtain a complete dictionary base matrix of the normalized intercept of the atmospheric humidity, the mass-weighted average diameter of the precipitation particles and the precipitation particle spectrum; S3, obtaining sparse coefficient vectors of each observation point by using a sparse representation model in combination with a likelihood function and a sparse prior calculation model according to the data obtained in S2, and optimizing by using the sparse representation model to realize optimization of the atmospheric temperature, the atmospheric humidity, the mass-weighted average diameter of the precipitation particles and the normalized intercept data of the precipitation particle spectrum; S4, processing the data optimized in S3 and obtaining a calculation result in combination with a structural similarity weight coefficient calculation model and a spatial weight coefficient calculation model, multiplying the calculation result and normalizing, and then obtaining a high spatial resolution three-dimensional data set of the atmospheric temperature, the atmospheric humidity, the mass-weighted average diameter of the precipitation particles and the normalized intercept of the precipitation particle spectrum according to a weighted projection formula; S5, filtering the data obtained in S4, processing the filtered data and calculating high spatial resolution phased array weather radar reflectivity factor three-dimensional data in combination with the data of S4 and a precipitation particle phase state probability calculation formula and a multi-phase radar reflectivity factor joint calculation formula; S6, generating a spatial position matrix according to the data collected in S1, combining the calculation of each point in the spatial position matrix with a direction guide coding vector column by column to form a direction guide coding matrix, and obtaining radar reflectivity factor data in a phased array weather radar stereoscopic scanning mode in combination with the data collected in S1 and the high spatial resolution phased array weather radar reflectivity factor three-dimensional data obtained in S5.

2. The method of claim 1, wherein: The collection of sparse sounding profile data sets and auxiliary data, and phased array weather radar spatial position and operating state data specifically comprises the following contents: Collecting sparse sounding profile data sets and auxiliary data obtained by an array of meteorological unmanned aerial vehicles; The sparse sounding profile dataset acquired by the meteorological UAV array includes: a total of M ground fixed points corresponding to the meteorological UAV array, and an atmospheric temperature profile dataset T_TR at different altitudes at the i-th meteorological UAV ground fixed point. i Atmospheric humidity profile dataset Q_TR i Mass-weighted average diameter profile dataset of precipitation particles Dm_TR i Normalized intercept profile dataset of precipitation particle spectra Nw_TR i , i=1,2,3,…M; the auxiliary data for the meteorological UAV array includes: the spatial location information of observation points at different altitudes on the i-th ground fixed point of the meteorological UAV, P i = (Lon_TR) i Lat_TR i Hig_TR i ), where Lon_TR i For longitude points, Lat_TR i Latitude point, Hig_TR i A vector representing different heights, i = 1, 2, 3, ..., M; The collection of spatial location and operational status data for phased array weather radar mainly includes: spatial location information of the phased array weather radar antenna feed P_radar=[lon_radar,lat_radar,hig_radar], where lon_radar, lat_radar, and hig_radar are longitude, latitude, and altitude, respectively; radar wavelength, radar radial range resolution delt_R, beamwidth delt_sita, and radar scanning sequence el_array=[el0,el1,el2,…,el] at the elevation layer. max ], el max Rmax_radar represents the maximum scanning elevation angle and the maximum unambiguous distance.

3. The method of claim 2, wherein: The generation of height uniform vectors and the generation of a spatial base matrix according to the collected data specifically comprises the following contents: A1, set the resolution and multiple of height layer respectively as delt_H and Num_H, generate the maximum height layer as Hig_max=Num_Hxdelt_H, and further generate the height uniform vector Z on each unmanned aerial vehicle ground fixed point i =hxdelt_H, wherein h=1, 2, 3, …, Num_H, i=1, 2, 3, …, M. A2, according to the collected P i = (Lon_TR i , Lat_TR i , Hig_TR i ), replace Hig_TR i in P i with Z i , generate each weather unmanned aerial vehicle ground fixed point after the height unified after the profile data space position information P i_uni = (Lon_TR i , Lat_TR i , Z i ); A3, set the observation point P j For any one of the spatial points, the longitude, latitude and height of the observation point P i_uni j are normalized in longitude, latitude and height dimensions respectively to obtain the normalized longitude , latitude and height , and the normalized ( ), j=1,2,3,..,MxNum_H;​ A4. Generating the observation point P according to the second order polynomial orthogonal basis function j of the spatial structure vector , denotes the first second order polynomial orthogonal basis function; A5, respectively, according to the method of steps A3 and A4, MxNum_H P j Perform traversal processing, and combine the processed MxNum_H spatial structure vectors to generate a spatial basis matrix Φs[ ] with a dimension of MxNum_Hx10.

4. The method of claim 3, wherein: The taking of the data collected in S1 as input, the obtaining of normalized atmospheric temperature profiles by adopting inverse distance weighted interpolation and normalization processing, and the obtaining of an attribute disturbance frequency spectrum base matrix in combination with a 1D discrete Fourier transform model specifically comprises the following contents: B1. Collecting the atmospheric temperature profile data set T_TR i at different height layers of the i-th meteorological unmanned aerial vehicle ground fixed point i As input, the inverse distance weighted interpolation method is used to interpolate T_TR i to the generated spatial information P i_uni The atmospheric temperature profile data after interpolation at each meteorological unmanned aerial vehicle ground fixed point is obtained at the spatial position of the target profile, denoted as ; B2. Calculate the profile data. The mean and standard deviation are denoted as mean( ) and std( Then, according to the Z-Score normalization formula, the profile data is normalized. After normalization, the normalized atmospheric temperature profile is obtained. ; B3, the atmospheric temperature profile is input into a 1D discrete Fourier transform model, the model is run, and the output of the model is taken modulo to obtain a spectrum modulo sequence DFT of the atmospheric temperature profile i = |DFT( )|; B4, the DFT i The modulus of the middle and high frequency part is set to zero, and the modulus of the low frequency part is reserved. The DFT i The first Ka low frequency components except the direct current component are respectively recorded as DFT i,2 , DFT i,3 , …, DFT i,Ka+1 , and the Ka low frequency components are combined into a one-dimensional vector, that is, the disturbance modal vector c i , Ka is the number of selected low frequency components; B5, traverse the method of B1-B4 steps to the atmospheric temperature profile data of M meteorological unmanned aerial vehicle ground fixed points on different height layers, obtain M disturbance modal vectors, respectively denoted as c1, c2, c3, …, c M , and splice the M disturbance modal vectors into the attribute disturbance frequency spectrum basis matrix Φc=[c1;c2;c3;…;c M ], the dimension of which is M×Num_H×Ka.

5. The method of claim 4, wherein: The complete dictionary matrix of the atmospheric temperature, humidity, mass-weighted mean diameter of the precipitation particles and the normalized intercept of the precipitation particle spectrum is obtained by updating the atmospheric temperature profile to the normalized intercept profile of the atmospheric humidity, mass-weighted mean diameter of the precipitation particles and the precipitation particle spectrum. C1, the generated normalized atmospheric temperature profile into the wavelet transform model, select the type of wavelet basis, set the decomposition level La, run the model, and the model output is the atmospheric temperature profile at the i-th point and wherein is the approximation coefficient of the La-th layer, is the detail coefficient of the r-th layer, r = 1, 2, 3, …, La; C2, according to the detail coefficient extraction disturbance energy calculation formula, extract the disturbance energy b of the i-th point at La decomposition layers i,r , r = 1, 2, 3, …, La; then, the disturbance energy b of all La decomposition layers at the i-th point i,1 , b i,2 , b i,3 , …, b i,La Combined into a multi-scale disturbance feature vector m i ; C3, the method of C1 and C2 steps is applied to the normalized atmospheric temperature profile of different height layers of M meteorological unmanned aerial vehicle ground fixed points, M multi-scale feature vectors are obtained, and the M multi-scale feature vectors are spliced into a multi-scale disturbance basis matrix Φm=[m1;m2;m3;…;m M ],m M is the Mth multi-scale feature vector, and the dimension is MxNum_HxLa; C4, the generated Φs, Φc and Φm generated in this step are combined to form the complete dictionary basis matrix Φ of atmospheric temperature T =[Φs||Φc||Φm], the atmospheric temperature profile data set is updated to the atmospheric humidity profile data set, the mass-weighted mean diameter profile data set of the precipitation particles, and the normalized intercept profile data set of the precipitation particle spectrum, respectively, and combined with Φs generated by A5 of S1 to generate the complete dictionary basis matrix Φ of the normalized intercept of atmospheric humidity Q , the complete dictionary basis matrix Φ of the normalized intercept of the mass-weighted mean diameter of the precipitation particles Dm , and the complete dictionary basis matrix Φ of the normalized intercept of the precipitation particle spectrum Nw .

6. The method of claim 5, wherein: The S3 specifically includes the following contents: S301, applying the step of B1 to the atmospheric temperature profile data on the M meteorological unmanned aerial vehicle ground fixed points, M interpolated atmospheric temperature profile data can be obtained, denoted as ; S302, Based on the generated complete dictionary base matrix of atmospheric temperature Φ T Using a sparse representation model, The observation data d at each observation point g g Use Φ T and sparse coefficient vector a g Representing d in the form of a linear combination g The input is fed into a computational model that combines the likelihood function and sparse priors. Running the model yields a sparse coefficient vector a for each observation point. g , will a g The input is fed into a Bayesian inference and maximum a posteriori estimation model with sparse constraints, and the model is run to obtain the optimized sparse coefficient vector. ; S303、based on the generated dictionary base Φ and the optimized , the atmospheric temperature data set is optimized by using the optimized sparse representation model, and the optimized atmospheric temperature data set is denoted as ; S304, Φ in this step T The atmospheric temperature profile dataset was updated to Φ. Q And atmospheric humidity profile dataset, Φ Dm And the mass-weighted average diameter profile dataset of precipitation particles, Φ Nw The datasets of normalized intercept profiles of precipitation particle spectra are used to optimize atmospheric humidity, mass-weighted average diameter of precipitation particles, and normalized intercept data of precipitation particle spectra, respectively. These are denoted as... , , .

7. The method of claim 6, wherein: The S4 specifically includes the following contents: S401、According to the generated optimized atmospheric temperature dataset The dataset is grouped according to the height layer to form a plurality of height layer two-dimensional sparse plane datasets T_Z h h = 1, 2, 3, …, Num_H; S402、According to the resolution of the set height layer delt_H, the high spatial resolution of the horizontal space is set as delt_R_H, a target three-dimensional grid Tar_grid with a spatial resolution of delt_R_H*delt_R_H*delt_H is generated, and T_Z h and the kth height layer data set of Tar_grid are denoted as T_Z k and Tar_grid_k, respectively, the maximum, median and minimum of T_Z k are calculated, and are denoted as max(T_Z k ), med(T_Z k ), and min(T_Z k ), respectively. S403, T_Z k With max(T_Z) k ), med(T_Z k ), min(T_Z k The inputs are combined with the inputs into the structural similarity weight coefficient calculation model. Running the model outputs T_Z. k The set of structural weight coefficients W1_T_Z for each sparse point k Calculate the relationship between a point pt and T_Z in Tar_grid_k. k The spatial distances between all sparse observation points are calculated, and the results are input into the spatial weight coefficient calculation model. The model is then run, and the output is T_Z. k The set of spatial weight coefficients for each sparse point in W2_T_Z k W1_T_Z k With W2_T_Z k Multiply and normalize to form the total weight coefficient set W_T_Z k According to the weighted projection formula, the atmospheric temperature value T_grid_pt at point pt in Tar_grid_k is calculated; S404, the method of S403 is applied to all points in Tar_grid_k, and the step is applied to all height layers in Tar_grid to obtain a high spatial resolution atmospheric temperature three-dimensional data set T_3D, and the atmospheric temperature data set of the step is sequentially updated to the atmospheric humidity, mass-weighted mean diameter of the precipitation particles and the normalized intercept of the precipitation particle spectrum data set, and high spatial resolution atmospheric humidity, mass-weighted mean diameter of the precipitation particles and normalized intercept of the precipitation particle spectrum three-dimensional data sets are obtained, respectively, and are denoted as Q_3D, Dm_3D and Nw_3D.

8. The method of claim 7, wherein the method is characterized by: The S5 specifically includes the following contents: S501, according to the generated T_3D, Q_3D, Dm_3D and Nw_3D data sets, setting the change range of Dm_3D and Nw_3D to [Dm_min, Dm_max] and [Nw_min, Nw_max] respectively, and removing the data in the Dm_3D and Nw_3D data sets that exceed the change range; S502, input the Dm_3D and Nw_3D data sets after removing the outliers into a 3-point × 3-point × 3-point median filter, and the filtered outputs are denoted as Dm_3D_f and Nw_3D_f, respectively; input the T_3D and Q_3D data sets into a spatial Gaussian smoothing filter, and the outputs are the filtered atmospheric temperature and humidity data sets, denoted as T_3D_f and Q_3D_f, respectively; S503, setting the atmospheric humidity threshold for precipitation occurrence as Q_th, marking all grid points with atmospheric humidity value greater than Q_th in Q_3D_f as 1, and marking the remaining grid points as 0 to generate a precipitation identification mask matrix Rain_Mask, and setting the phase of the precipitation particles as liquid, solid-liquid mixed and solid. S504, for any point P_s in the established target three-dimensional grid Tar_grid, combined with T_3D_f and Q_3D_f, obtain the atmospheric temperature and atmospheric humidity data of the point and , and then calculate the probabilities of the three phase states of liquid, solid-liquid mixed state and solid state of the space point P_s respectively using the precipitation particle phase state probability calculation formula, and record them as P_L s , P_M s , and P_I s respectively; S505, apply the step of S504 to all spatial points in the target three-dimensional grid Tar_grid, generate the probability matrixes of liquid, solid-liquid mixed and solid three phases, respectively recorded as P_L, P_M and P_I, set the related indexes of liquid, solid-liquid mixed and solid particles as alph_L, alph_M and alph_I, respectively, and then input [alph_L, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], [alph_M, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask], [alph_I, T_3D, Q_3D, Dm_3D, Nw_3D, Rain_Mask] and the collected radar wavelength wavelength into the phase state sensitive radar echo simulation system, run the system, and output the radar reflectivity factor three-dimensional data set Z_L, Z_M and Z_I of all points in the target three-dimensional grid Tar_grid when the precipitation particle phase is liquid, solid-liquid mixed and solid, respectively, calculate the high spatial resolution phased array weather radar reflectivity factor three-dimensional data Z_grid according to the calculated P_L, P_M and P_I, and Z_L, Z_M and Z_I, and according to the multi-phase radar reflectivity factor joint calculation formula.

9. The method of claim 8, wherein: The S6 specifically includes the following contents: S601, according to the collected phased array weather radar radial distance resolution delt_R, beam width delt_sita, maximum unambiguous distance Rmax_radar and radar elevation scanning sequence el_array, generate distance bin sequence bins_array=[delt_R, 2×delt_R, 3×delt_R, …, Rmax_radar], azimuth sequence az_array=[delt_sita, 2×delt_sita, 3×delt_sita, …, 360°], combine el_array, az_array and bins_array to generate the spatial position matrix P_echo=[el_array, az_array, bins_array] of the phased array weather radar echo in the three-dimensional scanning mode; S602, set a point in P_echo as P0, the spatial position of the point is [el_0, az_0, bin_0], wherein el_0, az_0 and bin_0 are the elevation angle, azimuth angle and distance bin of the point, respectively, then calculate the unit direction vector dr of the radar beam ray according to the unit direction vector calculation formula, calculate the polar coordinate angle coding E0_dir according to the polar coordinate angle coding formula, and calculate the three-dimensional relative coordinate coding E0_rel according to the three-dimensional relative coordinate coding calculation formula, combine the calculated E0_rel, E0_dir and dr to generate the azimuth direction coding vector E0 of the point P0. S603, apply the step of S602 to each point in P_echo, and combine the generated azimuthally steered encoding vector of each point by column to form an azimuthally steered encoding matrix E_ALL, input the collected spatial position information P_radar of the radar antenna feed, the generated Z_grid and the azimuthally steered encoding matrix E_ALL together into a volumetric scanning data implicit sampling simulator, run the simulator, and output the radar reflectivity factor data under the phased array weather radar stereoscopic scanning mode.

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